20.500.12556/DiRROS-20970 Opt2Vec - a continuous optimization problem representation based on the algorithm's behavior A case study on problem classification Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory. računalništvo algoritmi optimizacija true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2024 The Author(s) Neznano 2024-12-11 14:10:51 2024-12-11 14:10:52 2024-12-12 03:35:31 0000-00-00 00:00:00 2024 0 0 20 str. Art. 121134 Vol. 680 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-06-27 2024-06-28 2024-07-03 004 0020-0255 10.1016/j.ins.2024.121134 207123459 RAZ_Korosec_Peter_2024.pdf RAZ_Korosec_Peter_2024.pdf 1 BAEB45D90B046E2C204CA2E9E3EB3E92 f1872cae18470ce25348794aa4539eca180aee2cca02558160becc8f7e34ec2f bfdfe511-b7c2-11ef-ba3f-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=29669 Institut Jožef Stefan 0 0 0